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Temu_space

## Overview

Temu_space is a dataset for object detection tasks - it contains Sphere annotations for 213 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

INTRODUCTION:The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records. The training dataset contains all records from LILACS and IBECS databases at the Virtual Health Library (VHL) with a non-empty abstract written in Spanish. The URL used to retrieve records is as follows:http://pesquisa.bvsalud.org/portal/?output=xml&lang=es&sort=YEAR_DESC&format=abstract&filter[db][]=LILACS&filter[db][]=IBECS&q=&index=tw&We have filtered out empty abstracts and non-Spanish abstracts. The training dataset was crawled on 10/22/2019. This means that the data is a snapshot of that moment and that may change over time. In fact, it is very likely that the data will undergo minor changes as the different databases that make up LILACS and IBECS may add or modify the indexes. ZIP STRUCTURE:The training data sets contain 369,368 records from 26,609 different journals. Two different data sets are distributed as described below: - Original Train set with 369,368 records that also include the qualifiers, as retrieved from VHL. - Pre-processed Train set with the 318,658 records with at least one DeCS code and with no qualifiers. STATISTICS:Abstracts’ length (measured in characters)Min: 12Avg: 1140.41Median: 1094Max: 9428Number of DeCS codes per fileMin: 1Avg: 8.12Median: 7Max: 53 CORPUS FORMAT:The training data sets are distributed as a JSON file with the following format:{ ""articles"": [ { ""id"": ""Id of the article"", ""title"": ""Title of the article"", ""abstractText"": ""Content of the abstract"", ""journal"": ""Name of the journal"", ""year"": 2018, ""db"": ""Name of the database"", ""decsCodes"": [ ""code1"", ""code2"", ""code3"" ] } ]}

Note that the decsCodes field lists the DeCs Ids assigned to a record in the source data. Since the original XML data contain descriptors (no codes), we provide a DeCs conversion table (https://temu.bsc.es/mesinesp/wp-content/uploads/2019/12/DeCS.2019.v5.tsv.zip) with: - DeCs codes - Preferred descriptor (the label used in the European DeCs 2019 set) - List of synonyms (the descriptors and synonyms from both European and Latin Spanish DeCs 2019 data sets, separated by pipes) For more details on the Latin and European Spanish DeCs codes see: http://decs.bvs.br and http://decses.bvsalud.org/ respectively.Please, cite: Krallinger M, Krithara A, Nentidis A, Paliouras G, Villegas M. BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering. InEuropean Conference on Information Retrieval 2020 Apr 14 (pp. 550-556). Springer, Cham.

Temu Rotate

## Overview

Temu Rotate is a dataset for classification tasks - it contains Temu Y2Fl annotations for 756 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for STS-ca

  Dataset Summary

STS-ca corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. This dataset was developed by BSC TeMU as part of Projecte AINA, to enrich the Catalan Language Understanding Benchmark (CLUB). This work is licensed under a Attribution-ShareAlike 4.0 International License.

  Supported Tasks and Leaderboards

This dataset can be used to build and score semantic similarity models in Catalan.… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/sts-ca.

Dataset Card for CatalanQA

  Dataset Summary

This dataset can be used to build extractive-QA and Language Models. It is an aggregation and balancing of 2 previous datasets: VilaQuAD and ViquiQuAD. Splits have been balanced by kind of question, and unlike other datasets like SQuAD, it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times. This dataset was developed by BSC TeMU as part of Projecte AINA, to… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/catalanqa.

MOCAP_B_MOREremoveboat

## Overview

MOCAP_B_MOREremoveboat is a dataset for object detection tasks - it contains Human Boat Buoy WovI TemU annotations for 3,035 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Annotated corpora for MESINESP2 shared-task (Spanish BioASQ track, see https://temu.bsc.es/mesinesp2). BioASQ 2021 will be held at CLEF 2021 (scheduled in Bucharest, Romania in September) http://clef2021.clef-initiative.eu/

Introduction: These corpora contain the data for each of the subtracks of MESINESP2 shared-task:

[Subtrack 1] MESINESP - Medical indexing:

Training set: It contains all spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish. We have filtered out empty abstracts and non-Spanish abstracts. We have built the training dataset with the data crawled on 01/29/2021. This means that the data is a snapshot of that moment and that may change over time since LILACS and IBECS usually add or modify indexes after the first inclusion in the database. We distribute two different datasets:

Articles training set: This corpus contains the set of 237574 Spanish scientific papers in VHL that have at least one DeCS code assigned to them.

Full training set: This corpus contains the whole set of 249474 Spanish documents from VHL that have at leas one DeCS code assigned to them.

Development set: We provide a development set manually indexed by expert annotators. This dataset includes 1065 articles annotated with DeCS by three expert indexers in this controlled vocabulary. The articles were initially indexed by 7 annotators, after analyzing the Inter-Annotator Agreement among their annotations we decided to select the 3 best ones, considering their annotations the valid ones to build the test set. From those 1065 records:

213 articles were annotated by more than one annotator. We have selected de union between annotations.

852 articles were annotated by only one of the three selected annotators with better performance.

Test set: To be published

[Subtrack 2] MESINESP - Clinical trials:

Training set: The training dataset contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API. Clinical trials are not indexed with DeCS terminology, we have used as training data a set of 3560 clinical trials that were automatically annotated in the first edition of MESINESP and that were published as a Silver Standard outcome. Because the performance of the models used by the participants was variable, we have only selected predictions from runs with a MiF higher than 0.41, which corresponds with the submission of the best team.

Development set: We provide a development set manually indexed by expert annotators. This dataset includes 147 clinical trials annotated with DeCS by seven expert indexers in this controlled vocabulary.

Test set: To be published

[Subtrack 3] MESINESP - Patents: To be published

Files structure:

Subtrack1-Scientific_Literature.zip contains the corpora generated for subtrack 1. Content:

Subtrack1:

Train

training_set_track1_all.json: Full training set for subtrack 1.

training_set_track1_only_articles.json: Articles training set for subtrack 1.

Development

development_set_subtrack1.json: Manually annotated development set for subtrack 1.

Subtrack2-Clinical_Trials.zip contains the corpora generated for subtrack 2. Content:

Subtrack2:

Train

training_set_subtrack2.json: Training set for subtrack 2.

Development

development_set_subtrack2.json: Manually annotated development set for subtrack 2.

DeCS2020.tsv contains a DeCS table with the following structure:

DeCS code

Preferred descriptor (the preferred label in the Latin Spanish DeCS 2020 set)

List of synonyms (the descriptors and synonyms from Latin Spanish DeCS 2020 set, separated by pipes.

DeCS2020.obo contains the *.obo file with the hierarchical relationships between DeCS descriptors.

*Note: The obo and tsv files with DeCS2020 descriptors contain some additional COVID19 descriptors that will be included in future versions of DeCS. These items were provided by the Pan American Health Organization (PAHO), which has kindly shared this content to improve the results of the task by taking these descriptors into account.

For further information, please visit https://temu.bsc.es/mesinesp2/ or email us at encargo-pln-life@bsc.es